Signal recognition using neural network

I have .txt file contains number of lines represent zeros crossing. each line correspond to zero crossing according to specific three shold .
i have to train network on this file. then use it to detect other threshold?
any idea please. i do not have that experience with neural network to do that

Re: Signal recognition using neural network

Probability of detection and error depends on your bias for recognition and consequence of errors. According to Shannon you can predict this probability knowing the information SNR, BW, complexity ( modulation ) and "uniqueness" and redundancy of sequence. It also demands a matched receiver to match the spectral density of the signal to reject the spectrum of the noise, otherwise GIGO garbage in garbage out. From Fourier series we also can estimate the quantization noise and required number of samples to obtain a spectral density and a time domain unique pattern.

In theory, the logic signals have infinite SNR so only the time intervals of "zero crossings" or transition times with state levels may be possible to be uniquely detected with high certainty or low BER.

But for analog patterns with zero crossings of unknown SNR, we have a huge unknown for pattern recognition and you are tossing the quality signals in between. As you have told so far, there is yet no best approach for correlation.

Maybe you know better, but have not shared file or told us the specs for every variable I have mentioned, which is almost essential to a good solution. So in this situation one needs to know not just the zero crossing but the actual data in between. Pls share, and expand with details.

The method of detection may need cross-correlation, , timing histogram and sequence, spectral density and phase information, tolerance for pattern recognition etc then define the variables for a NN matrix or array to create a signature .